CHEMMEDCHEM FULL PAPERS DOI: 10.1002/cmdc.201400016

Computational Insight into p21-Activated Kinase 4 Inhibition: A Combined Ligand- and Structure-Based Approach Rui-Juan Li, Jian Wang, Zhen Xu, Wan-Xu Huang, Jia Li, Sheng-Fei Jin, Dong-Mei Zhao,* and Mao-Sheng Cheng*[a] p21-Activated kinase 4 (PAK4) is a serine/threonine protein kinase that plays important roles in a wide variety of human diseases including cancer. Targeting this kinase with specific inhibitors is of great interest in the treatment of cancer. In this study, PAK4 and its interaction with ATP-competitive inhibitors was investigated by a combined ligand- and structure-based approach. First, a ligand-based pharmacophore model was generated, consisting of five chemical features: a positive ionizable center, two hydrophobic groups, a hydrogen bond donor, and a hydrogen bond acceptor, which is consistent

with available SAR information. The characteristics of the active site were then described as a topological region and used in docking of nine selected inhibitors. Combination of the pharmacophore model and results from the docking studies allowed us to weigh the various pharmacophore features and to identify the positive ionizable center as a spacer rather than an essential point. This research led to the proposal of an interaction model inside the PAK4 active site and provided guidance for the design of more potent PAK4 inhibitors.

Introduction p21-Activated kinase 4 (PAK4) is a member of the p21-activated kinase (PAK) family of serine/threonine kinases.[1] The members of this family are downstream effectors of the Ras-related Rho-family GTPases Cdc42 and Rac.[2] There are six mammalian isoforms of PAKs, PAK1–6, which have been subdivided into two groups based on sequence similarities, domain structure, and regulation approaches: group I PAKs (PAK 1–3) and group II PAKs (PAK 4–6).[3] Much attention has been paid to PAK4, which is the most extensively and profoundly studied member of the group II PAKs.[4] PAK4 and other PAK isoforms share several structural characteristics such as a N-terminal Cdc42/Rac interactive binding region (CRIB), a highly conserved C-terminal catalytic serine/threonine kinase domain, and multiple prolinerich regions that serve as binding sites for SH3 domain-containing proteins.[5] PAK4 is identified as a cytoskeletal regulatory protein and regulates protein translation.[6] It is found that PAK4 is overexpressed in a wide variety of cancer cell lines, including prostate, breast, gall bladder, and stomach cancer cell lines.[7] Therefore, several efforts are ongoing to develop specific PAK4 inhibitors. To date, several chemical families of ATP-competitive inhibitors of PAK4 have been described. The broad-range kinase in[a] R.-J. Li, J. Wang, Z. Xu, W.-X. Huang, J. Li, S.-F. Jin, Prof. D.-M. Zhao, Prof. M.-S. Cheng Key Laboratory of Structure-Based Drug Design & Discovery Ministry of Education, School of Pharmaceutical Engineering Shenyang Pharmaceutical University, Shenyang 110016 (P.R. China) E-mail: [email protected] [email protected]

hibitor staurosporine and its derivative K252a can potently inhibit PAK4 activity.[8] Interestingly, it has also been found that cyclin-dependent kinase (CDK) inhibitors (23D, purvalanolA, dki) and the PDGFR-b inhibitor SU11652 also exhibit inhibitory activity toward PAK4.[8] Recently several pharmaceutical companies patented PAK4 inhibitors with new scaffolds, such as trisubstituted amino pyridinpyrimidinone core scaffolds from Afraxis, trisubstituted bicyclic heterocycles from AstraZeneca, as well as aminepyrazole and aminepyrrolopyrazole core fragments from Pfizer.[9–11] Unfortunately, the only PAK4 inhibitor in clinical development for tumor therapy, PF-03758309 launched by Pfizer, has been put on hold due to unfavorable pharmacokinetic properties.[12] Recently, a quinazoline derivative, LCH7749944, is reported as a modest inhibitor of PAK4.[13] Although great efforts have been made in developing PAK4 inhibitors, there is still lack of satisfactory kinase selectivity and druggability. Therefore, a clear idea of the structural requirements for PAK4 inhibitors is essential in the process of PAK4 inhibitor discovery. In this study, we constructed a ligand-based pharmacophore model on the basis of common features of PAK4 inhibitors. Then we systematically analyzed PAK4 crystal structures and topological distribution of the binding pockets in the catalytic cleft. The combination of ligand- and structure-based approaches demonstrates the major and secondary interaction points in the active site of PAK4, providing a useful strategy for designing novel and specific PAK4 chemotherapeutic agents.

Supporting information for this article is available on the WWW under http://dx.doi.org/10.1002/cmdc.201400016.

 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

ChemMedChem 2014, 9, 1012 – 1022

1012

CHEMMEDCHEM FULL PAPERS

www.chemmedchem.org

Results and Discussion

same distances between these features were deleted. Hypotheses with diverse configurations were selected according to the ranking scores and fit values. Finally, the hypotheses 01, Common feature pharmacophore modeling and validation 02, 04, and 08 were retained to select the best model. The ranking scores from a HipHop process cannot be used in seA common feature pharmacophore model explains how struclecting the best model, because they only describe the probaturally diverse ligands can bind to a common receptor site. bility of how well the training set compounds fitted the hyAmong the reported PAK4 inhibitors, we chose nine represenpothesis. Therefore, analysis of the fit values of the training set tative PAK4 inhibitors with diverse scaffolds as a training set. compounds against these hypotheses was carried out to Given the restricted range of PAK4 inhibition exhibited by the choose the best model. inhibitors, and the fact that these activities were obtained by The fit values of the training set compounds obtained by different procedures (Table 1), we were unable to generate mapping onto hypo 08 were generally higher than the correquantitative structure–activity relationships. However, the sesponding values for other hypotheses (Table 3). Thus, hypo 08 lected compounds represented a number of potent inhibitors was indicated as the best and final ligand-based model that consisted of one positive ionizable center Table 1. Activities of selected compounds derived from different PAK4 kinase domain (P), two hydrophobic groups (H1 and H2), one hydroenzymatic assays. gen bond donor (D), and one hydrogen bond acceptor (A) (Figure 1). The chemical features mapped onto [a] [b] [c] PAK4 [%] IC50 [nm] Ref. Index Name Ki [nm] the nine inhibitors are depicted in Figure 2. The posi1 JMC2012_hts 6 – – [14b] tive ionizable center (P) includes aliphatic amine 2 PF_03758309 18.7  6.6 – – [12a] groups; the hydrophobic groups (H1 and H2) are de3 53N 64 – – [14b] 4 PF_WO2007023382_35 11 – – [11b] fined by either aliphatic groups, such as methyl, di5 SU11652 – 34 – [8] methyl, ethyl, tertiary butyl, and cyclopropyl, or aro6 AZ_WO2006106326_14 – – 100 [10] matic moieties, such as phenyl, thiofuran, and benzyl, 7 Staurosporine – 0 – [8] or halogen atoms; the hydrogen bond donor (D) 8 PurvalanolA – 20 – [8] 9 23D – 56 – [8] contains hydrogen atoms in amino, benzimidazole, carboxamide, indolone, or isoindolone groups; and [a] Enzymatic activity of the PAK4 kinase domain was measured by its ability to catathe hydrogen bond acceptor (A) consists of either lyze the transfer of a phosphate residue from a nucleoside triphosphate to an amino acid side chain of a commercially available peptide. The PAK kinase domain Ki of each oxygen atoms in indolone, isoindolone, or carboxainhibitor was calculated on the basis of multiple experiments at different inhibitor mide groups, or nitrogen atoms with sp2 hybridizaconcentrations. [b] Enzymatic assays were carried out with controlled temperature tion in 2N-pyrrolopyrazole, 2N-aminepyrazole, 2N-inshifts, and PAK activity was expressed as the percent activity relative to control (1 % dazole, and 7N-purine cores. DMSO) reactions. [c] The in vitro PAK4 enzyme assay used scintillation proximity assay (SPA) technology to determine the ability of test compounds to inhibit phosphorylaThe final ligand-based model was overlaid on a mation with recombinant PAK4. PAK4 enzyme inhibition for a given test compound was jority of training set compounds. The small comexpressed as an IC50 value. pound 6 and the rigid compound 7 could not match

acting on the same target with the same mode of action (ATP mimetic).[14] Therefore, we employed the HipHop program of Discovery Studio software to provide 3D feature-based alignments for a set of preferably diverse and highly active compounds, without considering the activity quantitatively. The top-ten pharmacophore hypotheses consisting of five features had scores ranging from 94.613 to 104.258 kcal mol 1 (Table 2). Cluster analysis was used to estimate and sort the difference between the components and the locations of chemical features. These hypotheses could be roughly classified into two groups according to the pharmacophore features presented: PHDAA (01, 02, 03, 05, 09) and PHHDA (04, 06, 07, 08, 10). Within the two groups, the hypotheses were differentiated by the location of features, or the direction of hydrogen bond vectors, or both. Unreasonable hypotheses that specified the characteristics of a small number of compounds and had identical chemical characteristics and nearly the  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Table 2. Summary of the pharmacophore models generated by HipHop for PAK4 inhibitors. Hypothesis

Features[a]

Rank[b]

Direct Hit[c]

Partial Hit[d]

Max Fit

01 02 03 04 05 06 07 08 09 10

PHDAA PHDAA PHDAA PHHDA PHDAA PHHDA PHHDA PHHDA PHDAA PHHDA

104.258 101.604 101.399 99.256 98.771 96.719 96.382 95.969 95.703 94.613

111101111 111101111 111101011 111101111 111101011 111101111 111101110 111101110 111001111 111101110

000010000 000010000 000010100 000010000 000010100 000010000 000010001 000010001 000110000 000010001

5 5 5 5 5 5 5 5 5 5

[a] P, positive ionizable center; H, hydrophobic group; D, hydrogen bond donor; A, hydrogen bond acceptor. [b] The ranking score of training set compounds fitting the hypothesis. The higher the rank, the less likely it is that the compounds in the training set fit the hypothesis by a probability correlation. The best hypothesis shows the highest value. [c] Direct Hit indicates whether (“1”) or not (“0”) a molecule in the training set mapped every feature in the hypothesis. [d] Partial Hit indicates whether (“1”) or not (“0”) a particular molecule in the training set mapped all but one feature in the hypothesis. Numeration of molecules is from right to left in both Direct Hit and Partial Hit.

ChemMedChem 2014, 9, 1012 – 1022

1013

CHEMMEDCHEM FULL PAPERS

www.chemmedchem.org

on fit values. For inhibitors with an aminepyrrolopyrazole core, replacement of the benzyl group Training Set Compd Bioactivity Values DE [kcal mol 1][a] Scaled-Fit Values[b] with a smaller group (such as Ki [nm] PAK4 [%] IC50 [nm] hypo 01 hypo 02 hypo 04 hypo 08 methyl) decreased activity of in1 6 – – 9.794 0.742 0.746 0.734 0.794 hibitors toward PAK4 due to 2 18.7  6.6 – – 9.947 0.997 0.999 0.908 0.999 missing the first hydrophobic 3 64 – – 9.634 0.758 0.764 0.778 0.758 feature. Interestingly, the amine4 11 – – 3.434 0.600 0.733 0.675 0.868 pyrrolopyrazole and aminepyra5 – 34 – 9.378 0.666 0.689 0.735 0.815 6 – – 100 9.951 0.704 0.654 0.755 0.780 zole derivatives showed potent 7 – 0 – 9.310 0.574 0.666 0.646 0.754 activity toward PAK4 even with8 – 20 – 9.936 0.708 0.676 0.596 0.737 out a positive ionizable center. 9 – 56 – 9.908 0.629 0.686 0.627 0.770 Additionally, the inactive com[a] The relative energy difference to the global minimum calculated of the conformer mapping onto models. pounds were unable to match [b] Scaled-fit value of the minimum conformer used for mapping. onto the hydrogen bond donors and acceptors or the two hydrophobic features at the same time. Thus, the result of this validation clearly showed that the final ligand-based model had the capability of distinguishing the most active inhibitors from the inactive compounds, and that the presence of H1, D, A, and H2 chemical features in the final ligand-based model were essential for PAK4 inhibition. Table 3. The validation of hypotheses 01, 02, 04, and 08 by scaling the fit values, and DE values of the compounds in training set.

Common sets of water molecules

Figure 1. Selected common-feature pharmacophore model for PAK4 inhibitors consisting of two hydrophobic groups (H), a hydrogen bond donor (D), a hydrogen bond acceptor (A), and a positive ionizable center (P). Distances between the features are expressed in , with a tolerance sphere of radius  0.9 . For hydrogen bond donor D, the small sphere represents the hydrogen bond donor on training set compounds, and the big sphere represents the corresponding hydrogen bond acceptor on the protein residues. For hydrogen bond acceptor A, the small sphere represents the hydrogen bond acceptor on training set compound, and the big sphere represents the corresponding hydrogen bond donor on the protein residues.

the first hydrophobic group (H1). Without an aliphatic amine fragment, compound 8 could not match the positive ionizable center (P). These were taken into account in our pharmacophore hypothesis generation methodology (see Methods). Further validation of the final ligand-based model was conducted through investigating the structure–activity relationship (SAR) according to the relevance between matching pharmacophoric features with corresponding fit values and biological activity values. For this purpose, a test set consisting of five reported PAK4 inhibitors and four inactive compounds was mapped onto the final ligand-based model. Results of the model mapping onto inhibitors and fit values are shown in Table 4. Analysis of these fit values showed that the active inhibitors could be distinguished from the inactive ones based  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

To date, three crystal structures of the human PAK4 kinase catalytic domain in complex with different inhibitors (PDB codes: 2CDZ,[8] 2X4Z,[12a] 4APP)[14b] have been determined. Water molecules that mediated hydrogen bonding interactions between ligands and PAK4 were found in the binding sites of the crystal structures, which might affect the accuracy of ligand–protein docking. Generally, water molecules in the binding site are either bulk solvent molecules that are displaced by the ligand, or conserved molecules that bridge hydrogen bonding interactions between ligand and protein to stabilize the ligand–protein complex.[15] Confirmation of conserved water molecules in the binding site helps to fully identify the structural characteristics of the PAK4 active site. The three ligand–PAK4 complexes with an X-ray resolution ranging from 2.1 to 2.3  were superimposed with respect to the template structure with the highest resolution (PDB code: 2X4Z) to confirm common sets of water molecules in the binding site of PAK4. One water molecule (W2142 in 2X4Z, W2179 in 4APP, W2053 in 2CDZ) was observed in the same conserved position in all three complexes and involved in water-mediated hydrogen bonding interactions between ligands and PAK4. Thus, only one water molecule was identified as conserved and was included in the following native-docking studies to evaluate the effect on the accuracy of these docking simulations. Native-docking simulations To carry out native-docking simulations for PAK4, the three ligands extracted from the crystal structures of PAK4 were docked back into their corresponding protein structures. Two sets of native docking simulations were performed for each ChemMedChem 2014, 9, 1012 – 1022

1014

CHEMMEDCHEM FULL PAPERS

www.chemmedchem.org

complex: one in the presence of the conserved water molecule and the other one in the absence of the conserved water molecule.

The docking results are summarized in Table 5, and were evaluated by comparing the highest-ranked docked poses with the co-crystallized ones through GlideScores, root-meansquare deviations (RMSD), and protein–ligand interaction. In the 2X4Z complex, the RMSD values were nearly equal in the presence or absence of the water molecules and the hydrogen bonding interactions were identical except for the hydrogen bond formed between the ligand and the conserved water molecule. The GlideScore function predicted a slightly better scoring pose in the presence of water. The native-docking result of the 4APP complex was similar to that of the 2X4Z complex. In the case of the 2CDZ complex, in the presence or absence of the conserved water molecule, the relatively small cocrystallized ligand made hydrogen bonding interactions only with amino acid residue in the binding site. The difference between these two docking simulations was negligible. Therefore, the presence of the conserved water molecule can enhance the accuracy of docking simulation. Cross-docking simulations

Figure 2. 2D mapping of the ligand-based pharmacophore features onto the training set compounds. The features are as follows: hydrophobic group (H), hydrogen bond donor (D), hydrogen bond acceptor (A), and positive ionizable center (P). An attached (  ) indicates that a feature is not mapped.

Table 4. Mapping of a test set onto hypothesis 08. Compounds 1–5 are active PAK4 inhibitors, whereas compounds 6–9 are inactive. Test Set Compd

3D Mapping onto hypo 08

Fit Value DE [kcal mol 1]

Ki [nm]

1

9.737

0.994

41[a]

2

9.987

0.799

354[a]

3

 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

9.897

0.793

223[a]

Using information from the co-crystallized ligand to dock new ligands accurately is the major issue in docking. The best approach to test this is to dock a ligand from one protein crystal structure into the same target that has been co-crystallized with a different ligand. Three ligand–PAK4 complexes with the conserved water molecule were superimposed using the protein backbones. Each ligand was docked into each non-native protein structure, and the RMSD was calculated to measure docking reliability by comparing with the position in its native protein structure. Analysis of the results of the cross-docking simulations showed that the 2X4Z ligand had the smallest average RMSD and the lowest standard deviation (SD) and that the difference was significant when comparing the 2X4Z ligand with the 2CDZ (p < 0.01) and 4APP ligands (p < 0.05) (Figure 3). Thus 2X4Z was selected to analyze the characteristics of the ATP binding site of PAK4 and to dock the nine inhibitors of the pharmacophore training set. Characterization of the ATP binding site of PAK4 The ATP binding site in the 2X4Z complex consists of a broad and extended cleft. Further inspection of the active site reveals that there are five binding site pockets denoted A, B, C, D, and P and one water molecule W 2142 as shown in Figure 4. Pocket A consists of seven residues (Glu399, Gly400, Gly401, Ala402, Ile327, and Val335) and is relatively hydrophobic, and serves as an entrance for ligand binding. The gate width is 9.852  (between the Ile327 O atom and the Gly401 Ca atom). Pocket B is highly hyChemMedChem 2014, 9, 1012 – 1022

1015

CHEMMEDCHEM FULL PAPERS

www.chemmedchem.org

Table 4. (Continued) Test Set Compd

3D Mapping onto hypo 08

Fit Value DE [kcal mol 1]

Ki [nm]

4

9.954

0.800

3.5[b]

5

7.225

0.758

7.33[c]

6

8.816

0.580

–[d]

Phe397, Leu398), among which Glu396 is adjacent to pocket C. A relatively small pocket C is located in the deep region formed by the bC chain. Pocket C is covered by hydrophobic residues (Met395, Ala348, Val349, and Val379) of the b strands. Among these residues, Met395 is the gate keeper of the deep pocket. Thus this pocket is less conformationally flexible. The convex-shaped pocket D is positioned in the highly flexible G-loop that can adopt various conformations depending on the conformational state of the protein kinase and the presence of a ligand.[16] This pocket consists of six residues Gly333, Thr332, Ser331, Gly330, Glu329, and Gly328. Pocket P is covered by Lys442, Ser443, Asp444, Ser445, and Asp458 in the active loop which is highly flexible, hydrophilic, and solvent-exposed particularly in an active state. The conserved water molecule W2142 is located between pocket C and P and bridges neighboring charged residues (Lys350, Asp458) through hydrogen bond interactions. Inhibitor binding analysis

The ATP binding site in the 2X4Z complex was used to investigate the binding modes of the nine inhibitors in the pharmacophore training set, rationalizing the available SAR and evaluating the regional influence on biological activity. The sort of modeling pre7 3.414 0.551 –[d] viously performed, particularly the docking, really only estimates binding affinity, the biological data available are all inhibitory activities, which of course depend on affinity, but also on other factors such as solubility. In general, all studied inhibitors adopted an extended conformation, fitting the shape of the binding cleft (Figure 5). All inhibitors were in close 8 9.861 0.531 –[e] proximity to the hinge residues (Glu396, Phe397, Leu398) in pocket B and formed hydrogen bond interactions with the first and/or third hinge residue. However, detailed analysis of the complexes revealed more specific and crucial interactions. For inhibitors 1, 2, and 3 (Figure 5 A–C), the inter9 0 0.512 –[f] active modes with PAK4 are nearly identical except the p–p contact formed between the hinge residue Phe397 and aromatic groups of the inhibitors. This p–p contact significantly contributes to protein– [a] Carbonylamino pyrrolopyrazole compounds have activity as protein kinase inhibiligand interactions. The inhibitor activity declines tors, including as inhibitors of PAK4.[11c] [b] Amino pyrrolopyrazole compounds have when the number of p–p interactions decreases activity as protein kinase inhibitors, including as inhibitors of PAK4.[11a] [c] Pyrimidine (Table 6). Residue Phe397 apparently plays an imporamino pyrazole compounds have activity as protein kinase inhibitors, including inhibitant role in inhibitor binding. The geminal dimethyl tors of PAK4.[11b] [d] PIR-36, PIR-35; these highly selective allosteric inhibitors for PAK1 are not active against group II PAKs.[19] [e] AG879, not active against group II PAKs. groups of inhibitors 2 and 3 fit pocket C better than [f] Emodin, no data available concerning its activity at PAK. the 1H-indazolyl group of inhibitor 1. The small and less flexible pocket C could augment the selectivity of inhibitors toward PAK4 through the penalty of steric clashes. The methyl-thiophene fragment of inhibitor 2 and the phenoxy phenyl of inhibitor 3 fit pocket A drophobic, providing a major site for ligand binding. The better than the 1H-benzimidazolyl group of inhibitor 1. Pocket b strands and the N-lobe provide hydrophobic residues to A is the entrance for ligand binding and thus could enhance form this region. It is rimmed with the hinge residues (Glu396,  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

ChemMedChem 2014, 9, 1012 – 1022

1016

CHEMMEDCHEM FULL PAPERS

www.chemmedchem.org

Table 5. Summary of the native docking. PDB Code Resolution [] 2CDZ 2X4Z 4APP

2.3 2.1 2.2

Water Set GlideScore [kcal mol 1] RMSD [] 8.466 11.562 12.800

0.48 0.38 0.42

H-bond Residues[a]

No Water Set GlideScore [kcal mol 1] RMSD []

Leu398 Leu398, Glu396, W2142, Asp458 Leu398, Glu396, W2179, Asp458

8.685 10.971 10.095

0.54 0.38 0.43

H-bond Residues

Leu398 Leu398, Glu396, Asp458 Leu398, Glu396, Asp458

[a] Residues or water molecules forming interactions with the docked poses through hydrogen bonds.

Figure 3. Analysis of cross-docking simulations. For each protein structure of PAK4 (PDB codes: 2CDZ, 2X4Z, 4APP), five docked poses per ligand were selected to calculate the average RMSD and standard deviation (SD); *p < 0.01, **p < 0.05. Error bars: mean  SD.

Figure 4. The PAK4 active site in complex with a ligand (X4Z) and its hydrophobic surface. The protein is displayed by secondary structure. The water molecule is represented by CPK type (sphere toward the upper right). The inhibitor is shown as in stick representation. The subpockets A, B, C, D, and P are indicated by the dashed boundary lines.

Through comparing the interactive modes and biological activities of inhibitors 2 and 4 (Figure 5 B, D and Table 6), we can inhibitor selectivity toward PAK4 by filtering molecules for size propose that the conserved water molecule and the G-loop do and shape. All three inhibitors 1, 2, and 3 are potent PAK4 innot have important influences on inhibitor affinities toward hibitors, whereas inhibitors 2 and 3 are more selective than inPAK4, but these two anchoring points can improve PAK4 inhibhibitor 1, indicating pocket A and C play important roles in difitor selectivity. The charge–charge interactions formed beferentiating inhibitor selectivity for PAK4. tween the basic nitrogen atoms of inhibitors and the deprotonated aspartic acid (Asp444 or Asp458) of pocket P are not in a fixed position. In addition, the binding modes and biological activities of inhibitors 8 and 9 Table 6. Summary of the docking studies of nine inhibitors in the training set. (Figure 5 H, I and Table 6) show that the charge– charge interaction in pocket P is less important in Training Set Compd Panel Activity GlideScore XSCORE achieving high affinity inhibitor binding. The aliphat[kcal mol 1][d] DG [kcal mol 1][e] pKd[f] ic amines will be protonated under acidic condition [a] 1 A 6 13.66 9.38 6.88 in cancer cells (pH < 3), attacking cancer cells by 13.24 9.59 7.03 2 B 18.7  6.6[a] 11.61 10.06 7.38 3 C 64[a] charge–charge interactions. Thus, the pocket P 11.05 8.70 6.37 4 D 11[a] could act as a spacer and be used to improve inhibi[b] 8.48 8.98 6.58 5 E 34 tor physicochemical properties and selectivity in [c] 6.68 8.09 5.93 6 F 100 lead optimization, especially for an inactive-state 9.30 9.37 6.87 7 G 0[b] 8.93 8.57 6.28 8 H 20[b] kinase target. 8.10 8.24 6.04 9 I 56[b] The binding modes and biological activities of in[a] Ki [nm]. [b] Percent activity of PAK4 [%]. [c] IC50 [nm]. [d] GlideScore function scores hibitors 5, 7, and 9 (Figure 5 E, G, I and Table 6) show in Schrçdinger suite (version 2013). [e] XSCORE function scores. [f] The predicted disthat both residues Glu396 and Leu398 appear to be sociation constant values by XSCORE function. significant for inhibitory potency, and that the inhib 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

ChemMedChem 2014, 9, 1012 – 1022

1017

CHEMMEDCHEM FULL PAPERS

www.chemmedchem.org

Figure 5. Docking of compounds 1 (A), 2 (B), 3 (C), 4 (D), 5 (E), 6 (F), 7 (G), 8 (H), and 9 (I) inside the human PAK4 active site. The hydrophobic surface of the pocket is also indicated.

itor structures should be larger to fully fit the broad and extended cleft to enhance the affinities of inhibitors. In conclusion, pockets A, B, and C are essential regions for PAK4 inhibitory activity, the flexible pocket D and conserved water molecule are anchoring points influencing the selectivity of inhibitors toward PAK4, and pocket P is a spacer to improve selectivity in lead optimization. For the three hinge residues, the hydrogen bond providers (Glu396 and Leu398) and the p– p provider (Phe397 with an aromatic ring shielding one side of pocket B) are essential residues for PAK4 inhibitory activity. Comparison of the docking results with the pharmacophore model: toward an integrated model within the PAK4 active site To assess the ligand-based pharmacophore model relative to the docking results, the inhibitors in their “bioactive” conformations (that is, bound to PAK4) were superimposed to the ligand-based pharmacophore model. Both conformations were generally quite well overlaid for all the nine studied inhibitors  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

except for inhibitors 5 and 6. This research confirmed that the proposed ligand-based pharmacophore model was able to fit the binding cavity and match well with the topology of the active site. Four of the five pharmacophore features correspond to the constant interaction positions with key amino acids in the binding site of PAK4 identified during the molecular docking analysis. Indeed, the first hydrophobic group (H1) is situated at hydrophobic pocket A. The second hydrophobic group (H2) is pinpointed accurately at the small pocket C. The hydrogen bond donor (D) locates to pocket B and forms an interaction with the carbonyl oxygen atom of Leu398. The hydrogen bond acceptor (A) also locates to pocket B and accepts a hydrogen atom from the nitrogen of Leu398. The positive ionizable center (P) is positioned in the polar hydrophilic pocket P, but the feature is not mapped by all the “bioactive” conformations of inhibitors, as the position of charge–charge interactions is not constant for the “bioactive” conformations of inhibitors inside the PAK4 active site. Therefore, it is suggested that the positive ionizable center (P) is not an essential feature for optimal interaction with PAK4, but serves as ChemMedChem 2014, 9, 1012 – 1022

1018

CHEMMEDCHEM FULL PAPERS a spacer moiety that is common to a majority of the studied inhibitors. More information from a broad range of active inhibitors in the training set is captured in the common feature pharmacophore model. Nevertheless, the pharmacophore model generated may not correctly reflect the interaction mode between the inhibitors and PAK4, which derives from the possible defects of algorithms for the ligand conformation generation. To refine the pharmacophore model, structural information originating from the binding site of PAK4 and the docking results was incorporated in the pharmacophore generation. The docked poses of the nine studied inhibitors were added to the conformational models and imported into Discovery Studio with the same parameter options as the initial common feature pharmacophore modeling. Eight new pharmacophore hypotheses were produced. They all consisted of four features and had scores ranging from 44.207 to 51.244 kcal mol 1. Based on hierarchical clustering, three groups can be distinguished among these: HHDA, HHAA, and HHHA. Deeper analysis revealed that the sixth hypothesis (H1D1A1H2) (Figure 6 A)

Figure 6. A) Refined pharmacophore hypothesis 06. B) Refined pharmacophore hypothesis 08. The pharmacophore features are as follows: hydrogen bond acceptor (A), hydrogen bond donors (D1 and D2), hydrophobic features (H1 and H2). Distances between the features are expressed in , with tolerance spheres of radii  0.9 .

and the eighth hypothesis (H1A1D2H2) (Figure 6 B) displayed the best correlation between the inhibitor mapping and the docking results. Among these features, D2 formed a hydrogen bonding interaction with the carbonyl oxygen atom of Glu396. For inhibitor 6, the conformation mapping onto hypo 08 coincided well with its docking result, whereas mapping onto hypo 06 had steric clashes with residues (Met395, Gly330) in the binding site (Figure S1A, S1B). The mapped conformations of inhibitors 7, 8, and 9 onto hypo 06 were consistent with their docking conformations (Figure S2A, S2B, S3A, S3B, S4A, S4B). It should be stressed that, the isoindolone fragment of inhibitor 7 staurosporine forms two hydrogen bonding interactions with the Leu398. The same glutamic acid and leucine residues are contained in the ATP binding site of other protein kinases (for example, PKA, CDK2, YANK1, CTR1, P70S6K1, MK2, ASK1, DAP, PKC)[17] and form two hydrogen bonding interac 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

www.chemmedchem.org tions with the isoindolone fragment of the pan kinase inhibitor staurosporine. In our study, the conformation of staurosporine mapping onto hypo 08 superimposes onto the PAK4 active site, making two hydrogen bonding interactions with Glu396 and Leu398, respectively. However, staurosporine bumps against several residues (Val335, Gly330, Glu329, Gly328) and the conserved water molecule (Figure S2B), which is disadvantageous for affinity. Therefore, the two refined pharmacophore models (H1H2D1A) and (H1H2AD2) can closely correlate the presence of chemical features of inhibitors with the interactions formed with the key amino acids in the active site. To validate the two refined pharmacophore models, three receptor–ligand based pharmacophore models were developed and are depicted in Figure S5. Pharmacophore hypotheses built by the receptor–ligand based method are directly derived from the interactions between the ligands and PAK4 in complex, but the models with numerous features are too restrictive and contain very limited information on single ligands. Through comparing three receptor–ligand based pharmacophore models, the two refined pharmacophore models are ideal pharmacophore models that not only properly reflect the interaction modes between the ligands and receptor, but also contain information from more active compounds. Based on the analysis of the docking results, two anchoring points, characteristic of the pyrrolopyrazole derivatives, can be proposed: 1) a urea carbonyl oxygen atom moiety, able to interact with the conserved water molecule; 2) the phenyl and/ or benzyl group located at the G-loop and forming s–p interaction with Lys350. These points were not emphasized during the pharmacophore generation because they are not shared by all the inhibitors. However, these additional secondary interaction points could be taken advantage of in the design of new inhibitors. Thus, the proposed target-based pharmacophore model involves five principal chemical features and three secondary ones. For the PAK4 active site, the binding pockets A, B, and C are the principal regions and pocket D and P are the secondary ones (Figure 7).

Conclusions To understand the binding interactions between PAK4 and its diverse inhibitors, we constructed a ligand-based pharmacophore model consisting of five chemical features: a positive ionizable center, two hydrophobic groups, a hydrogen donor, and a hydrogen acceptor (PHHDA). The model was consistent with available SAR information. A conserved water molecule was observed and used in docking simulations. The characteristics of the PAK4 active site were described for the topological regions, and then the active site was used to study the binding modes of nine inhibitors in the pharmacophore training set. Combination of the ligand-based pharmacophore model and the docking results allowed us to weigh the relevance of the five pharmacophoric features and the interaction modes in the active site. We identified that the positive ionizable center plays the role of a spacer rather than an essential anchoring point. This was further confirmed by the two refined pharmacophore models (H1H2D1A) and (H1H2AD2), obtained from ChemMedChem 2014, 9, 1012 – 1022

1019

CHEMMEDCHEM FULL PAPERS

www.chemmedchem.org age. The receptor–ligand based pharmacophore was generated with the automated pharmacophore generation program LigandScout 2.02. Docking studies were performed with Glide in Schrçdinger.

Figure 7. Refined pharmacophore hypothesis superimposed onto the PAK4 active site. The five key pharmacophore features (H1D1AD2H2) point well into three major binding pockets (A, B, C). The three secondary pharmacophore features identified through docking studies are indicated in bold text.

the addition of the docked conformations to the conformational models. Two additional anchoring points were added to the model. One was the hydrogen bond acceptor forming hydrogen bonding interactions with the conserved water molecule W2142 that also solvates neighboring charged residues (Asp458, Lys350) through hydrogen bond interactions; the other one was the hydrophobic group that formed effective hydrophobic interactions with the residues of the G-loop and formed s–p interactions with Gly350. The combined approach resulted in the proposal of an interaction model inside the PAK4 active site, involving five principal pharmacophore features: two hydrophobic groups, a hydrogen acceptor and two hydrogen donors, and three secondary ones: a positive ionizable center, a hydrogen bond acceptor, and a hydrophobic group. Crucial anchoring points inside the binding site include Glu396, Phe397, and Leu398 which play an important role in the affinity of PAK4 inhibitors. The secondary anchoring point is the conserved water molecule W2142 which influences PAK4 selectivity. The principal subpockets in the PAK4 active site include pockets A, B, and C, whose sizes are fitted fully to enhance the binding affinities of PAK4 inhibitors. The secondary pockets P and D serve as spacers and have an effect on PAK4 selectivity and improve inhibitor physicochemical properties. Thus the optimal inhibitors should form interactions with the key anchoring points and have large chemical structures to fit fully the broad and extended cavity of PAK4. The present study is a fundamental step toward the characterization of human PAK4 and its interaction with ligands, which provides a meaningful model for PAK4 lead discovery and optimization.

Experimental Section All computational experiments were conducted on a Dell PowerEdge R910 workstation. The common feature pharmacophore was generated using the common feature pharmacophore generation protocol implemented in the Discovery Studio 3.0 software pack 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Common feature pharmacophore modeling and validation: The inhibitors in the training set were constructed using sketcher in Discovery Studio, and standard 3D structures were generated and minimized to the nearest local minimum using the molecular mechanics CHARMM force field. For all compounds, the Poling algorithm was applied to generate a maximum of 255 diverse conformations with an energy threshold of 20 kcal mol 1 above the calculated energy minimum for every compound in the database. These conformations were generated using conformation generation protocol running with the best/flexible conformation generation option as available in Discovery Studio 3.0. All nine compounds in the training set associated with their conformations were subsequently submitted to the common feature pharmacophore generation protocol HipHop module in Discovery Studio. The hypotheses were produced by comparing the collection of conformational models and the selection of chemical features shared among the training set molecules, and distributed within a three-dimensional (3D) space. In this study, feature mapping revealed that four chemical feature types, that is, hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic group (H), positive ionizable center (P), could effectively map all molecules in our training set. Maximum pharmacophore hypotheses was set to ten. The minimum feature option was 4, and maximum feature option was 6. Under the hypothesis generation method, the tolerance factor was set to a value of 0.8 instead of 1.0, and the minimum inter-feature distance was set to a value of 1.5  instead of 2.97 . As there were small molecules in the input set, features in the pharmacophore were close together. With the aim of acquiring the best model, the pharmacophore generation was conducted by altering the control parameters based on the characteristics of inhibitors in the training set. As compounds 2 and 3 were the most potent inhibitors and also used in crystal structures of the human PAK4 complexes, the principal value of 2 and MaxOmitFeat value of 0 were assigned to these two most active compounds, meaning all the features in the two molecules would be considered in building a hypothesis space. The principal value of 2 and MaxOmitFeat value of 1 were assigned to compounds 1 and 5, implying that the molecules are either to be used or not when building the hypothesis depending on the setting of other parameters such as misses, feature misses, and complete misses. Although compound 8 was co-crystallized with PAK4, the structure was smaller and its inhibitory activity was less than for the other inhibitors. Therefore, the principal value of 1 and MaxOmitFeat value of 1 were assigned to compound 8 and the rest of the compounds. As there were small and rigid molecules in the training set, the ‘misses’ value was assigned as 3 instead of 0, allowing three molecules in the training set not to map to all features in generated hypotheses. The values of feature misses and complete misses were set as 1 and 0, respectively. The validity of the model was evaluated by scaling fit values of the training set molecules and a test set comprised of other PAK4 inhibitors with a wide range of PAK4 inhibitory activity, and less active PAK4 inhibitors. Ligand Profiler in Discovery Studio was used in mapping the selected molecules onto the best models. According to the simulation of chemical features of molecules mapped to the pharmacophoric features of the best models, fit values was scaled. The higher the score value, the better the match. Finally, the best models were used to match some chemical structures correlating to the structure–activity relationship (SAR). ChemMedChem 2014, 9, 1012 – 1022

1020

CHEMMEDCHEM FULL PAPERS Selection of common sets of water molecules: The crystal structure (PDB code: 2X4Z) for PAK4 with the highest X-ray resolution was chosen as the reference complex, and the other complexes of the same protein were aligned to it by superimposing all Ca atoms. Water molecule clusters present within a 5.0  radius with respect to the ligand in the template structure were visually inspected to select a common set of water molecules for PAK4. Visual inspection was slightly extended beyond the cutoff radius to view the complete water clusters, especially when they were not fully seen at the edge of the cutoff radius (5.0 ). Water molecules within 1.5  from each other were considered to be in the same cluster. The orientations of the selected water molecules were optimized by energy minimization in the absence of any bound ligand, keeping all protein atoms rigid and fixing the position of the oxygen atom of each water molecule, as described below. Water molecules are identified with reference to their numbers in the PDB file of the reference structure. Protein structure preparation: The PAK4–ligand co-crystallized structures were processed with the protein preparation wizard in the Schrçdinger suite. The protein structure integrity was adjusted and modified, and missing residues and loop segments near the active sites were added using Prime. All hydrogen atoms were added, followed by making sure that multiple bonds orders were defined correctly and that hydrogens were properly added for amino acid residues and the co-crystallized ligand. Only the selected water molecules that were conserved and bridged interactions between the ligands and PAK4 (see above) were retained, and the solvent water molecules and other cofactors were deleted. The protonation and tautomeric states of Asp, Lys, and His were adjusted to match a pH of 7.4. Afterward, hydrogen bond sampling was checked and adjusted by adjusting the water molecule orientations. All hydrogen atoms and the PAK4–ligand complexes were subjected to accurate energy evaluation using an all-atom force field, OPLS_2005, restrained minimization with convergence of heavy atoms to an RMSD of 0.3 . Ligand preparation: Ligands were extracted from the superimposed complexes, and all bonds and atom types were checked for consistency. Hydrogen atoms were added, and ionization states were determined, considering a pH of 5.0–9.0. The nine molecules used to construct the common feature pharmacophore were imported into Maestro 9.0 and prepared by using LigPrep from the Schrçdinger suite which included adding all hydrogen atoms, checking the bond order and atom types, adjusting an appropriate protonation state for aliphatic amines at the default pH range 5.0– 9.0, generating low energy ring conformations, and optimizing the geometries at the OPLS_2005 force field using a steepest-descent algorithm. Characterization of the PAK4 active site: 2X4Z, the most appropriate protein site evaluated through cross-docking, was used to characterize the PAK4 active site. The PAK4 active site was defined as a sphere of 5  radius centered on the co-crystallized inhibitor (X4Z). The hydrophobic surface of the active site was calculated in Discovery Studio 3.5 Visualizer. Docking studies: Docking simulations of the native ligands and the nine inhibitors in the PAK4 active site were carried out using Glide XP in the Schrçdinger suite (version 2013).[20] Glide used a hierarchical series of filters to search for possible locations of ligands in the active-site region of the receptor. The shape and properties of the receptor were represented on a grid with several different sets of fields that provided progressively more accurate scores of the ligand poses. Conformational flexibility was handled in Glide  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

www.chemmedchem.org with an extensive conformational search, augmented with a heuristic screen that rapidly eliminated unsuitable conformations. Finally, the energy-minimized poses were rescored using Schrçdinger’s proprietary GlideScore scoring function. GlideScore was based on ChemScore, but included a steric-clash term, adding buried polar terms devised by Schrçdinger to penalize electrostatic mismatches, and making modifications to other terms including van der Waals energy, Coulomb energy, the hydrophobic interactions, hydrogen bonding term and/or metal binding term, penalty for buried polar groups and freezing rotatable bonds, and polar interactions in the active site. The extra-precision (XP) mode of Glide combined a powerful sampling protocol with the use of a custom scoring function designed to identify ligand poses that would be expected to have unfavorable energies, based on well-known principles of physical chemistry. The assumption was that only active compounds would have available poses that avoided these penalties and also received favorable scores for appropriate hydrophobic contact, hydrogen bonding, and other interactions between the protein and the ligand. The chief purpose of the XP method was to weed out false positives and to provide a better correlation between good poses and good scores.

As the XP Glide scoring function was based on enforcement of physical chemical principles to a much greater degree than that employed in many other scoring functions, appropriate protein and ligand preparations were particularly critical. Receptor grid generation required a “prepared” structure: an all-atom structure with appropriate bond orders and formal charges. The receptor grid generation panel was used to set up and generate the receptor grid. The prepared protein–ligand complex was imported into Maestro 9.6, and was defined as the receptor structure excluding the co-crystallized ligand whose position determined the location of the active site (the position of the 2X4Z-grid is x = 20.61, y = 20.87, z = 58.45) and the size of the active site (inner box = 10   10   10 , outer box = 20   20   20 ) through setting the options in each tab of this panel. The OPLS_2005 force field was used for grid generation. Ligand docking jobs could not be performed until the receptor grids had been generated. The previously calculated receptor grids and prepared ligands were used to carry out docking simulations using the ligand docking panel from the Glide submenu in the applications menu. XP was selected as the docking precision with ligands docked flexibly. Specifying the parameters, which included keeping 5000 poses per ligand for the initial phase of docking, setting the rough-score cutoff as 100.0 kcal mol 1 for keeping initial poses, keeping the best 900 poses per ligand for energy minimization with the OPLS_2005 non-bonded interaction grid, was significant for accurate docking simulations. The energy minimization stage of the docking algorithm minimized the energy of poses that passed the selection of initial poses. The distance-dependent dielectric model, in which the effective dielectric “constant” is the supplied constant multiplied by the distance between the interacting pair of atoms, was set to 2.0, as Glide’s sampling algorithms were optimized for this value. The maximum 100 steps was adopted using the conjugate gradient minimization algorithm. Other parameters were set with the default values. The core was specified in terms of a reference ligand to constrain the docking of other ligands or to calculate the RMSD from the defined core for all docked ligands. The constraints tab and similarity tab were not set. The OPLS_2005 force field was used for docking. Finally, the output tab controlled the final output of ligand poses that passed successfully through Glide’s various scoring stages, writing out at most 10 000 ligand poses per docking run and at most 100 poses per ligand. Then at least 100 poses ChemMedChem 2014, 9, 1012 – 1022

1021

CHEMMEDCHEM FULL PAPERS per ligand were used to perform post-docking minimization to improve the geometry of the poses. Maestro 9.6 and Discovery Studio 3.5 Visualizer were used to visualize the docking results. The experimental scoring function XSCORE[18] was used to re-score the input ligand in its modeled position, which required accurate initial placement of the ligand with respect to the receptor. van der Waals interactions, hydrogen bonding, deformation penalty, and hydrophobic effects between the receptor and the ligand were taken into account in XSCORE. Finally, the binding affinity of a given protein–ligand complex was expressed in pKd units, where Kd represented the dissociation constant (pKd = log Kd). This function was able to predict the binding free energy with an acceptable success rate for molecule docking tasks and protein-ligand systems.[18b] Receptor–ligand based pharmacophore modeling with LigandScout: The interaction models of three crystal structures of PAK4 in complex with 23D, X4Z, and 53N (PDB codes: 2CDZ, 2X4Z, 4APP) were analyzed by means of LigandScout 2.02. Default parameter settings were used. Finally, according to the actual interaction models and positions in the active site, three precise receptor–ligand based pharmacophoric models were constructed.

Acknowledgements The authors thank Inte:Ligand for allowing us to use their software (LigandScout) free of charge for this study. The research was financially supported by the National Natural Science Foundation of China (Grants 81230077, 81102379, 81001092, and J1210029), the National High Technology Research and Development Program of China (Grant 2007AA02Z305) and the Education Department of Liaoning Province (Grant L2011174). Keywords: anticancer agents · molecular docking · p21activated kinase 4 · pharmacophore models [1] M. A. Sells, J. Chernoff, Trends Cell Biol. 1997, 7, 162 – 167. [2] a) S. I. Ellenbroek, J. G. Collard, Clin. Exp. Metastasis 2007, 24, 657 – 672; b) A. B. Jaffe, A. Hall, Annu. Rev. Cell Dev. Biol. 2005, 21, 247 – 269; c) G. H. Xiao, A. Beeser, J. Chernoff, J. R. Testa, J. Biol. Chem. 2002, 277, 883 – 886. [3] Z. M. Jaffer, J. Chernoff, Int. J. Biochem. Cell Biol. 2002, 34, 713 – 717. [4] a) A. E. Dart, C. M. Wells, Eur. J. Cell Biol. 2013, 92, 129 – 138; b) J. J. Crawford, K. P. Hoeflich, J. Rudolph, Expert Opin. Ther. Pat. 2012, 22, 293 – 310. [5] R. K. Jha, C. E. Strauss, Cell Logist 2012, 2, 69 – 77. [6] a) S. Baldassa, A. M. Calogero, G. Colombo, R. Zippel, N. Gnesutta, J. Cell. Physiol. 2010, 224, 722 – 733; b) C. Dan, A. Kelly, O. Bernard, A. Minden, J. Biol. Chem. 2001, 276, 32115 – 32121. [7] a) S. Chen, T. Auletta, O. Dovirak, C. Hutter, K. Kuntz, S. El-ftesi, J. Kendall, H. Han, D. D. Von Hoff, R. Ashfaq, A. Maitra, C. A. Iacobuzio-Donahue, R. H. Hruban, R. Lucito, Cancer Biol. Ther. 2008, 7, 1793 – 1802; b) X. Li, Q. Ke, Y. Li, F. Liu, G. Zhu, F. Li, Int. J. Biochem. Cell Biol. 2010, 42, 70 – 79; c) C. M. Wells, A. D. Whale, M. Parsons, J. R. Masters, G. E. Jones, J. Cell Sci. 2010, 123, 1663 – 1673; d) A. Minden, ISRN oncology 2012, 694201; e) A. Minden, Cell Logist 2012, 2, 95 – 104.

 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

www.chemmedchem.org [8] J. Eswaran, W. H. Lee, J. E. Debreczeni, P. Filippakopoulos, A. Turnbull, O. Fedorov, S. W. Deacon, J. R. Peterson, S. Knapp, Structure 2007, 15, 201 – 213. [9] D. Campbell, S. G. Durn, B. Vollrath, W. Wade, (Afraxis Inc., La Jolla, CA, USA) Int. PCT Pub. No. WO2010071846, 2010. [10] K. Daly, N, Heron, A. Hird, S. Ioannidis, J. W. Janetka, P. Lyne, J. Scott, D. Toader, M. Vasbinder, D. Yu, Y. Yu, (AstraZeneca UK Ltd.) Int. PCT Pub. No. WO2006106326, 2006. [11] a) D. Bouzida, L. Dong, C. Guo, Y. Hong, H. Li, J. T. Marakovits, A. Yang, J. Zhang, (Pfizer Inc.) Int. PCT Pub. No. WO2006072831, 2006; b) L. Dong, C. Guo, M. C. Johnson, H. Li, J. T. Marakovits, I. J. Mcalpine, (Pfizer Inc.) Int. PCT Pub. No. WO2007023382, 2007; c) L. Dong, C. Guo, Y. Hong, M. C. Johnson, S. E. Kephart, H. Li, I. J. Mcalpine, J. G. Tikhe, A. Yang, J. Zhang, (Pfizer Inc.) Int. PCT Pub. No. WO2007072153, 2007. [12] a) B. W. Murray, C. Guo, J. Piraino, J. K. Westwick, C. Zhang, J. Lamerdin, E. Dagostino, D. Knighton, C. M. Loi, M. Zager, E. Kraynov, I. Popoff, J. G. Christensen, R. Martinez, S. E. Kephart, J. Marakovits, S. Karlicek, S. Bergqvist, T. Smeal, Proc. Natl. Acad. Sci. USA 2010, 107, 9446 – 9451; b) B. J. Ryu, H. Lee, S. H. Kim, J. N. Heo, S. W. Choi, J. T. Yeon, J. Lee, J. Y. Cho, S. Y. Lee, Mol. Cell. Biochem. 2013, DOI: 10.1007/s11010-013-19288; c) R. L. Mileshkin, T. Blumenkopf, A. Breazna, S. Darang, J. Davison, J. Gallo, J. W. Goldman, D. D. Wang, S. Zhang, S. G. Eckhardt (ASCO Annual Meeting Abstracts), J. Clin. Oncol. 2011, 29, e13607; d) M. K. Siu, H. Y. Chan, D. S. Kong, E. S. Wong, O. G. Wong, H. Y. Ngan, K. F. Tam, H. Zhang, Z. Li, Q. K. Chan, S. W. Tsao, S. Stromblad, A. N. Cheung, Proc. Natl. Acad. Sci. USA 2010, 107, 18622 – 18627. [13] J. Zhang, J. Wang, Q. Guo, Y. Wang, Y. Zhou, H. Peng, M. Cheng, D. Zhao, F. Li, Cancer Lett. 2012, 317, 24 – 32. [14] a) J. Eswaran, M. Soundararajan, R. Kumar, S. Knapp, Trends Biochem. Sci. 2008, 33, 394 – 403; b) C. X. Guo, I. McAlpine, J. H. Zhang, D. D. Knighton, S. Kephart, M. C. Johnson, H. T. Li, D. Bouzida, A. L. Yang, L. M. Dong, J. Marakovits, J. Tikhe, P. Richardson, L. C. Guo, R. Kania, M. P. Edwards, E. Kraynov, J. Christensen, J. Piraino, J. Lee, E. Dagostino, C. Del-Carmen, Y. L. Deng, T. Smeal, B. W. Murray, J. Med. Chem. 2012, 55, 4728 – 4739. [15] R. Thilagavathi, R. L. Mancera, J. Chem. Inf. Model. 2010, 50, 415 – 421. [16] J. J. Liao, J. Med. Chem. 2007, 50, 409 – 424. [17] a) S. Couty, I. M. Westwood, A. Kalusa, C. Cano, J. Travers, K. Boxall, C. L. Chow, S. Burns, J. Schmitt, L. Pickard, C. Barillari, P. C. McAndrew, P. A. Clarke, S. Linardopoulos, R. J. Griffin, G. W. Aherne, F. I. Raynaud, P. Workman, K. Jones, R. L. M. van Montfort, Oncotarget 2013, 4, 1647 – 1661; b) M. P. Martin, R. Alam, S. Betzi, D. J. Ingles, J. Y. Zhu, E. Schonbrunn, ChemBioChem 2012, 13, 2128 – 2136; c) T. Sunami, N. Byrne, R. E. Diehl, K. Funabashi, D. L. Hall, M. Ikuta, S. B. Patel, J. M. Shipman, R. F. Smith, I. Takahashi, J. Zugay-Murphy, Y. Iwasawa, K. J. Lumb, S. K. Munshi, S. Sharma, J. Biol. Chem. 2010, 285, 4587 – 4594; d) G. Bunkoczi, E. Salah, P. Filippakopoulos, O. Fedorov, S. Muller, F. Sobott, S. A. Parker, H. Zhang, W. Min, B. E. Turk, S. Knapp, Structure 2007, 15, 1215 – 1226; e) K. W. Underwood, K. D. Parris, E. Federico, L. Mosyak, R. M. Czerwinski, T. Shane, M. Taylor, K. Svenson, Y. Liu, C. L. Hsiao, S. Wolfrom, M. Maguire, K. Malakian, J. B. Telliez, L. L. Lin, R. W. Kriz, J. Seehra, W. S. Somers, M. L. Stahl, Structure 2003, 11, 627 – 636. [18] a) R. Wang, L. Lai, S. Wang, J. Comput.-Aided. Mol. Des. 2002, 16, 11 – 26; b) C. Obiol-Pardo, J. Rubio-Martinez, J. Chem. Inf. Model. 2007, 47, 134 – 142. [19] S. W. Deacon, A. Beeser, J. A. Fukui, U. E. Rennefahrt, C. Myers, J. Chernoff, J. R. Peterson, Chem. Biol. 2008, 15, 322 – 331. [20] Schrçdinger User Manuals, Glide v 9.3, Schrçdinger LLC, New York, NY (USA), 2013.

Received: January 6, 2014 Published online on March 18, 2014

ChemMedChem 2014, 9, 1012 – 1022

1022